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200 papers
Charting the Way with TRAX for Chronic Disease Surveillance.
Baumgartner CJ, Karras BT, Petyak A … +1 more , Bair J
AMIA Annu Symp Proc · 2024 · PMID 41726423
In December 2022, the Washington Healthcare Forum Board and Washington Department of Health (DOH) senior leadership agreed to develop a plan for improving public health chronic disease surveillance in Washington state. T...
In December 2022, the Washington Healthcare Forum Board and Washington Department of Health (DOH) senior leadership agreed to develop a plan for improving public health chronic disease surveillance in Washington state. Through a joint planning committee process, we created a plan for a cooperatively governed, technologically flexible, secure platform for sharing data. The approach, known as TRAX (Transformational Repository & Analytics eXchange), recognizes the importance of effective governance alongside health information exchange (HIE). TRAX governance partners identified priority conditions, diabetes and hypertension, to scope early projects (using anonymous patient-level longitudinal data). Leveraging public health HIE advances, like the eCR Now FHIR App, and the national Trusted Exchange Framework and Common Agreement (TEFCA) infrastructure, is reducing development time and administrative burden. Using nationally available standards and infrastructure, TRAX is an adaptable approach; a reproducible data sharing solution for cross-jurisdictional healthcare systems; and a potential national model for chronic disease surveillance.
APEA: A Type 1 Diabetes Self-Management Ambient-AI Assistance Tool that Bridges Trajectory Prediction, Interactive Explanation, and Just-in-Time Adaptive Intervention Action.
Chen KY, Tallon EM, Shyu CR
AMIA Annu Symp Proc · 2024 · PMID 41726422
Helping patients self-managing diseases like type 1 diabetes (T1D) requires informatics tools delivering real-time predictions with explainable, actionable guidance. However, many healthcare AI solutions lack actionable...
Helping patients self-managing diseases like type 1 diabetes (T1D) requires informatics tools delivering real-time predictions with explainable, actionable guidance. However, many healthcare AI solutions lack actionable recommendations and user-friendly explanations, limiting clinical impacts. We introduce APEA, a pediatric T1D self-management mbient-AI assistance tool, integrating glucose multi-trajectory-scenarios rediction, interactive, context-aware large language model xplanations, and just-in-time adaptive intervention policy optimization for ctionable real-time suggestions through reinforcement learning. Using T1DEXIP dataset (262 pediatric T1D patients, multi-center), our results showed improved glucose control outcomes: 45% over human management, 69% over infusion-pump management. Although constrained by small sample size and severe class imbalance, APEA addresses healthcare AI implementation gaps by bridging what might happen, what can be done about it, and why it makes clinical sense. APEA offers a transferable framework for other chronic conditions that demand continuous, personalized, just-in-time adaptive interventions.
A Framework for an Intelligent Social Engagement Support System: Identifying and Addressing Challenges at Multiple Levels to Reduce Health Disparities.
Reynolds TL, Algrain H, de Leon L … +2 more , Parker B, Stockwell I
AMIA Annu Symp Proc · 2024 · PMID 41726421
Increasingly managed care organizations (MCOs) - health plans that offer covered health services through networks of healthcare providers and hospitals - are recognizing the role of social drivers of health on both healt...
Increasingly managed care organizations (MCOs) - health plans that offer covered health services through networks of healthcare providers and hospitals - are recognizing the role of social drivers of health on both health outcomes and unnecessary healthcare utilization (e.g., emergency department visits) and are taking actions. They are employing social care navigators (SCNs) and screening for social needs such as food, housing, and transportation. However, individuals with social needs can experience barriers to completing the screening and to receiving social care when referred. Additionally, the volume of people with social needs means that SCNs have high caseloads. Without guidance on how to prioritize their efforts, the people who have the highest social needs may not be identified or receive SCN support. To address the many problems, we developed a framework for an intelligent social engagement support system. This framework builds on the Health Care System domain of the National Institutes on Minority Health and Health Disparities' Health Disparities Research Framework as a foundation, leverages learning health system and community-engaged approaches, and incorporates machine learning at key points to create an adaptable, multi-level framework that is intended to have numerous positive outcomes, including reducing health disparities in socially-driven health outcomes. We discuss how we are using this framework and how we envision it could be adapted to different MCO contexts.
VaxKG: Integrating The Vaccine Ontology And VIOLIN For Advanced Vaccine Queries And LLM-Powered Chat Systems.
Yeh FL, Asato M, Zheng J … +1 more , He YO
AMIA Annu Symp Proc · 2024 · PMID 41726420
Vaccine research faces challenges in integrating diverse biomedical datasets. While the Vaccine Investigation and Online Information Network (VIOLIN) provides comprehensive vaccine data, implemented in traditional relati...
Vaccine research faces challenges in integrating diverse biomedical datasets. While the Vaccine Investigation and Online Information Network (VIOLIN) provides comprehensive vaccine data, implemented in traditional relational models limit complex analysis. Similarly, the Vaccine Ontology (VO) offers standardized semantic frameworks but lacks comprehensive empirical data. This study addresses these limitations by developing the Vaccine Knowledge Graph (VaxKG) that integrates VIOLIN's dataset with VO's standardized terminology. Using Neo4j, we transformed 12 core VIOLIN tables into a graph structure enriched with VO concepts. The resulting knowledge graph comprises 28,123 VIOLIN data nodes and 101,282 VO resource nodes, connected by 412,865 relationships. Our comparative analysis of Brucella and Influenza vaccines demonstrates VaxKG's ability to enable complex semantic queries and reveal insights unavailable from either resource alone. We further demonstrate VaxKG's utility through VaxChat, a large language model application that leverages the VaxKG as Retrieval-Augmented Generation (RAG) for intuitive vaccine information access.
Re-designing inpatient nursing notes shared with families: an opportunity to enhance family-centered care delivery.
Ruppel H, Khan A, Mintor R … +6 more , Nguyen J, McNamara M, Luo B, Kelly M, Cato K, Froh EB
AMIA Annu Symp Proc · 2024 · PMID 41726419
This modified explanatory sequential mixed methods study sought to inform redesign of nursing notes in the electronic health record. In the context of OpenNotes and patient and family access to nursing notes via the inpa...
This modified explanatory sequential mixed methods study sought to inform redesign of nursing notes in the electronic health record. In the context of OpenNotes and patient and family access to nursing notes via the inpatient portal, redesigning nursing notes offers an opportunity to enhance family-centered care delivery and reduce nurses' documentation burden. We analyzed data on note views via the inpatient portal for 258,841 nursing notes; annotated the contents of 100 nursing notes; and conducted interviews with 18 families and 8 nurses. Our findings support recommendations for more specific care plans, eliminating redundancies, and emphasizing nursing care and expertise otherwise absent from the patient chart. The results of this descriptive study lay the groundwork for pilot testing new nursing note structures.
Identifying Latent Patterns of Intimate Partner Violence Using Electronic Health Records and Association Rule Mining.
Tabaie A, Wyand SA, Cao F … +5 more , Losonczy L, Bennett SS, Blackall LM, Potts A, Fong A
AMIA Annu Symp Proc · 2024 · PMID 41726418
Intimate partner violence (IPV) remains a significant public health concern, yet its identification in emergency department (ED) settings is often challenging, as IPV-related ICD-10 are infrequently and inconsistently us...
Intimate partner violence (IPV) remains a significant public health concern, yet its identification in emergency department (ED) settings is often challenging, as IPV-related ICD-10 are infrequently and inconsistently used. This study employs electronic health record (EHR) data and association rule mining to identify latent patterns indicative of IPV among female patients in ED. We applied the Apriori algorithm to identify associations between co-occurring health conditions in confirmed IPV survivors and non-IPV patients. The analysis revealed distinct patterns in both groups, with IPV survivors showing stronger links between physical injuries, mental health conditions (e.g., depression, suicidal ideation), and socioeconomic stressors. Non-IPV cases were primarily associated with anxiety disorders and family-related stressors. These findings highlight the potential of informatics-driven approaches to improve IPV detection by revealing subtle clinical signals that may notbe overtly disclosed. Our work paves the way for developing data-driven tools to enhance IPV screening and clinical decision-making in ED.
10-Year Risk Prediction of Higher-Grade AV Block in Patients with First-Degree AV Block.
Kim DW, Kwon H, Park JW … +5 more , Park HN, Kwon OS, Han C, Kim Y, Yoon D
AMIA Annu Symp Proc · 2024 · PMID 41726417
First-degree atrioventricular (AV) block has traditionally been considered benign, but emerging evidence suggests it may indicate a risk of progression to higher-degree AV block. This study developed and externally valid...
First-degree atrioventricular (AV) block has traditionally been considered benign, but emerging evidence suggests it may indicate a risk of progression to higher-degree AV block. This study developed and externally validated a machine learning model to predict AV block progression using ECG-derived parameters. A retrospective cohort study was conducted using 12-lead ECG data from Severance Hospital (development) and Yongin Severance Hospital (external validation). The model was trained with six ECG-derived parameters (RR interval, P duration, PR segment, PR interval, QRS duration, QT interval), along with age and sex, using a Random Forest algorithm. It achieved an AUROC of 0.823 (AUPRC 0.719) in internal validation and AUROC 0.808 (AUPRC 0.894) in external validation. SHAP analysis identified PR segment, QRS duration, and age as key predictors. This model enables early risk stratification for AV block progression using widely available ECG parameters, supporting clinical decision-making.
A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub.
Yu JK, Martínez-Romero M, Horridge M … +2 more , Akdogan MU, Musen MA
AMIA Annu Symp Proc · 2024 · PMID 41726416
In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusabili...
In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusability by aligning heterogeneous data under standardized representations, benefiting both repository curators responsible for upholding data quality standards and consumers who require unified datasets. However, data harmonization is difficult in practice, requiring significant domain and technical expertise. We present a software framework to facilitate principled and reproducible harmonization protocols. Our framework implements a novel strategy of building harmonization transformations from parameterizable primitive operations, such as the assignment of numerical values to user-specified categories, with automated bookkeeping for executed transformations. We establish our data representation model and harmonization strategy and then report a proof-of-concept application in the context of the RADx Data Hub. Our framework enables data practitioners to execute transparent and reproducible harmonization protocols that align closely with their research goals.
Measuring Accuracy of ConsultBot, Hybrid AI Tool, in Interpreting Blood Gas Results.
Meka P, Silvers CT, Gunapati B … +1 more , Liu Q
AMIA Annu Symp Proc · 2024 · PMID 41726415
Arterial and venous blood gases are complex tests used in inpatient settings. While interpretation of blood gas results is algorithmic, it is time consuming and error prone when done manually. Our study measures the accu...
Arterial and venous blood gases are complex tests used in inpatient settings. While interpretation of blood gas results is algorithmic, it is time consuming and error prone when done manually. Our study measures the accuracy of ConsultBot, in automated interpretation of blood gases. To determine proportional accuracy of ConsultBot, using rule-based logic combined with a large language model (LLM), in interpreting blood gases. ConsultBot was tested in an IRB-approved single-arm trial using a dataset of 101 blood gas results and compared with a predetermined answer key. The tool's performance was assessed using proportional accuracy, sensitivity, and Cohen's Kappa. ConsultBot achieved a 98% (99/101)[CI 93%-100%] proportional accuracy across the study dataset. Sensitivity was 98%[CI 93%-99%]. Cohen's Kappa of 0.97 suggests high degree of agreement between ConsultBot's interpretations and answer key. ConsultBot's evaluation yielded promising results, demonstrating potential in clinical-decision support for blood gas interpretations.
Designing Technology-Assisted Interventions for Justice-Impacted Black American Women.
McCall T, Lowery A, Massey B … +4 more , Swaminath M, Afrose S, Saunders M, Wang KH
AMIA Annu Symp Proc · 2024 · PMID 41726414
This study explores the challenges faced by justice-impacted Black women during their reintegration into society, with a focus on mental health care access and the potential for technology-assisted interventions to addre...
This study explores the challenges faced by justice-impacted Black women during their reintegration into society, with a focus on mental health care access and the potential for technology-assisted interventions to address barriers. Participants from focus groups emphasized significant obstacles, including inadequate mental health resources during incarceration, insufficient post-release support, and barriers such as discrimination, lack of insurance, and transportation issues. When designing technology-assisted interventions, such as the Welcome Home app, additional considerations for justice-impacted Black women include trauma-informed design, tiered support systems, integration with electronic health records, privacy protection, and culturally tailored content. The study underscores the importance of culturally relevant, user-centered digital solutions to improve health outcomes and facilitate the successful reintegration of Black women impacted by the criminal legal system. Apps that provide a sense of community promote engagement, which may improve health outcomes.
Fair Multi-modal Canonical Correlation Analysis: A Neuroimaging Study of Alzheimer's Disease.
Zhou Z, Tong B, Hou B … +3 more , Davatzikos C, Long Q, Shen L
AMIA Annu Symp Proc · 2024 · PMID 41726413
This study addresses fairness concerns in Multi-modal Canonical Correlation Analysis (MCCA), a technique for analyzing relationships across multiple datasets. We introduce Fair MCCA (F-MCCA), which mitigates bias by opti...
This study addresses fairness concerns in Multi-modal Canonical Correlation Analysis (MCCA), a technique for analyzing relationships across multiple datasets. We introduce Fair MCCA (F-MCCA), which mitigates bias by optimizing for both correlation performance and demographic fairness. Our method quantifies disparities using Correlation Disparity Error (CDE) and employs a multi-objective optimization framework to derive projection matrices that achieve consistent correlation levels across sensitive groups. We validate F-MCCA on neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative using sex as the sensitive attribute. Experiments demonstrate that F-MCCA substantially improves fairness metrics with minimal correlation performance sacrifice. In downstream classification tasks, F-MCCA reduces demographic parity difference and equalized odds difference while maintaining comparable accuracy to standard approaches. Results confirm that our method effectively balances analytical performance with fairness considerations, supporting more unbiased healthcare applications of multi-modal data analysis.
A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.
Tang LA, Kast KA, Walsh CG
AMIA Annu Symp Proc · 2024 · PMID 41726412
Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. We developed a predictive model to guide treatment...
Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. We developed a predictive model to guide treatment selection for OUD. Models predicted the probability of treatment response for each medication, which we defined as the absence of an adverse outcome during hospitalization and within 90 days of hospital discharge. Models considered electronic health record (EHR) and ZIP-level data. We constructed generalized linear regression, random forest, gradient boosted machines, and deep learning models and tested different combinations of EHR and ZIP-level data using early and late fusion methods. EHR-only models performed better than ZIP-only models did. ZIP-level data did not significantly improve the performance of EHR-only models. Models consistently recommended buprenorphine over methadone. Future work should explore different approaches to modeling OUD treatment response and capturing relevant social and external factors.
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare.
Zhang H, Lou Q, Wang Y
AMIA Annu Symp Proc · 2024 · PMID 41726411
Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information....
Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare.
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide.
Duan Z, Wei K, Xue Z … +5 more , Zhou J, Yang S, Ma S, Jin J, Li L
AMIA Annu Symp Proc · 2024 · PMID 41726410
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging ta...
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG). We apply this framework to semaglutide for weight loss using data from Reddit. Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time. These findings are further validated through comparison with adverse events reported in the FAERS database, providing important patient-centered insights into semaglutide's side effects that complement its safety profile and current knowledge base of semaglutide for both healthcare professionals and patients. Our work demonstrates the feasibility of using LLMs to transform social media data into structured KGs for pharmacovigilance.
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping.
Pungitore SA, Yadav S, Subbian V
AMIA Annu Symp Proc · 2024 · PMID 41726409
Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive manual data review. While machine learnin...
Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive manual data review. While machine learning and natural language processing advancements have helped, further improvements are needed. Few studies have explored using Large Language Models (LLMs) for these tasks despite known advantages of LLMs for text-based tasks. To facilitate further research in this area, we developed an evaluation framework, Evaluation of PHEnotyping for Observational Health Data (PHEONA), that outlines context-specific considerations. We applied and demonstrated PHEONA on concept classification, a specific task within a broader phenotyping process for Acute Respiratory Failure (ARF) respiratory support therapies. From the sample concepts tested, we achieved high classification accuracy, suggesting the potential for LLM-based methods to improve computational phenotyping processes.
Using National COVID cohort collaborative (N3C) data to explore impact of COVID-19 infection on kidney function in patients receiving lithium therapy.
Tian Y, Yocum AK, Li J … +3 more , Oliver He Y, Richesson RL, McInnis MG
AMIA Annu Symp Proc · 2024 · PMID 41726408
Lithium is a drug primarily used to treat psychiatric disorders and has shown significant efficacy in treating bipolar disorder (BD) and major depressive disorder (MDD). Although lithium can stabilize emotional fluctuati...
Lithium is a drug primarily used to treat psychiatric disorders and has shown significant efficacy in treating bipolar disorder (BD) and major depressive disorder (MDD). Although lithium can stabilize emotional fluctuations and prevent the onset of depressive and manic episodes, case reports of patients treated with lithium developing kidney dysfunction after being infected with SARS-CoV-2 emerged during the COVID-19 pandemic. We used the National COVID Cohort Collaborative (N3C) dataset to investigate the relationship between patients with lithium exposure and with SAR-Co-V-2 infection, and kidney dysfunction. Using data from March 1, 2021, to September 30, 2022, patients were classified as whether they were infected with SARS-CoV-2. We used the estimated glomerular filtration rate (eGFR) to observe whether there are significant differences between the two groups. Our findings suggest that the impact of lithium treatment and COVID-19 on kidney function may not be significant, consistent with most other studies' findings.
HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction.
Ding Z, Dong X, Liu Y … +5 more , Ma T, Zhao X, Wong R, Rosenthal RN, Wang F
AMIA Annu Symp Proc · 2024 · PMID 41726407
Drug overdose, mostly from opioids, is a continuing crisis in the US. Highly accurate models for early detection of opioid overdose (OD) risk are crucial for early intervention and prevention. While deep learning has sho...
Drug overdose, mostly from opioids, is a continuing crisis in the US. Highly accurate models for early detection of opioid overdose (OD) risk are crucial for early intervention and prevention. While deep learning has shown promise in using electronic health records (EHRs) for OD risk prediction, its clinical utility is often limited by challenges with data sparsity, heterogeneity, label imbalance, and lack of interpretability. We present HIBERT, a hybrid BERT model that combines the transformer model with deep clustering. HIBERT uses a multiple BERT architecture integrating specialized BERT modules for distinct EHR feature categories, and incorporates deep significance clustering to generate clinically meaningful risk stratification. HIBERT outperforms conventional and state-of-the-art models based on evaluation with the Health Facts database and identifies four distinct risk clusters, in addition to ranked critical features. It provides actionable, personalized OD risk assessment with improved interpretability.
Leveraging Large Language Models for Thyroid Nodule Information Extraction and Matching Across Medical Reports.
Lee D, Amara D, Beon C … +6 more , Swee S, Radhachandran A, Athreya S, Ivezic V, Arnold C, Speier W
AMIA Annu Symp Proc · 2024 · PMID 41726406
Accurate extraction of thyroid nodule features from radiology and pathology reports is clinically essential for guiding patient management decisions, such as surgical intervention or active surveillance. However, manual...
Accurate extraction of thyroid nodule features from radiology and pathology reports is clinically essential for guiding patient management decisions, such as surgical intervention or active surveillance. However, manual data extraction from electronic health records is labor-intensive and prone to inter-rater variability. To address this challenge, we evaluated open-source large language models (LLMs) for automating the extraction and matching of these critical nodule features. Using a retrospective dataset of 451 ultrasound and pathology report pairs, we developed an annotation schema capturing nodule characteristics. Two LLMs-Llama-3.3 70B and QwQ-32B-were benchmarked against manual annotations. Both models demonstrated near-perfect extraction accuracy for clinically relevant features such as location, size, and biopsy results. Notably, QwQ-32B achieved an F1 score of 0.987 on the complex multi-step reasoning task of matching nodules across reports. Our findings suggest integrating LLMs into clinical annotation workflows can significantly reduce clinician workload and inter-rater variability while maintaining high accuracy.
DGSurv: Dynamic Graph-Based Multimodal Learning for Interpretable Cancer Survival Prediction.
Shahabi S, Cui Z, Liu R … +2 more , Carlson J, Liu Y
AMIA Annu Symp Proc · 2024 · PMID 41726405
Multimodal learning in cancer research offers transformative potential for enhancing medical care and guiding clinical decisions. Most analyses rely on unimodal inputs or employ simplistic multimodal fusion techniques, w...
Multimodal learning in cancer research offers transformative potential for enhancing medical care and guiding clinical decisions. Most analyses rely on unimodal inputs or employ simplistic multimodal fusion techniques, which do not optimally integrate the diverse data types. Additionally, there is a critical need for enhanced interpretative methods to fully exploit the depth of multimodal patient data. To address these issues, we propose DGSurv, a novel multimodal learning approach that utilizes a graph neural network (GNN) to dynamically map inter-modality relationships for cancer survival prediction. We demonstrate the utility of our proposed approach on cancer survival prediction, highlighting its potential to inform more accurate clinical decision-making. We perform empirical evaluations on four cancer datasets from The Cancer Genome Atlas Program (TCGA) and demonstrate that DGSurv outperforms existing fusion techniques. For interpretability, our study advances multimodal cancer analysis by effectively harnessing the full spectrum of multimodal data and significantly boosting its interpretability.
Shedding Light on the Invisible Work of Peer Recovery Support Specialists through the Design of a Point of Care Technology.
Pater JA, Andrews E, Drouin M … +6 more , Flanagan M, Albright D, Pfafman R, Carroll J, Hess E, Toscos TR
AMIA Annu Symp Proc · 2024 · PMID 41726404
Peer Recovery Support Specialists (PRSSs) are certified professionals that provide social, informational, and logistical supportto people in recovery fromsubstance use disorders. In this study, we report the findings fro...
Peer Recovery Support Specialists (PRSSs) are certified professionals that provide social, informational, and logistical supportto people in recovery fromsubstance use disorders. In this study, we report the findings from design work completed with a teamof PRSSsto define workflowand informationalneeds for the developmentofa novel point of care software. Through our qualitative research we uncovered a significant amount of undocumented work that is necessary for a PRSS to provide individualized support to their clients. Thus, much PRSS work is not quantified and rendered "invisible." We present technology design strategies that help quantify these tasks and illuminate the complexity of PRSS work.
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